library(haven)
library(dplyr)
allPatients = file.path("../","All_Data_Patientstudy.sav")
allDataPDGroups = file.path("../", "All_Data_PD_GroupPatientstudy.sav")
allDataPatientStudyDS = read_sav(allPatients)
allDataPDGroupsStudyDS = read_sav(allDataPDGroups)
glimpse(allDataPatientStudyDS)
## Observations: 34
## Variables: 90
## $ Subject <dbl> 504, 505, 506, 507, 508, 510, 511, 5…
## $ Group <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ Sex <chr+lbl> 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0,…
## $ Age <dbl> 69, 44, 62, 75, 73, 70, 60, 65, 65, …
## $ Length_Parkinson <dbl> 24, 20, 12, 20, 3, 6, 6, 10, 11, 9, …
## $ Medication <dbl> 600, 300, 500, 500, 800, 600, 200, 1…
## $ H_Y <dbl> NA, NA, 3.0, 3.0, 4.0, 2.0, NA, 2.0,…
## $ STN_Stimulation <dbl> 1, 1, 1, 0, NA, 0, 0, 0, 0, 0, 0, 0,…
## $ Handedness <dbl> 100.0, 100.0, 100.0, 95.8, 100.0, 91…
## $ MMSE <dbl> 23, 30, 24, 25, 26, 27, 29, 25, 30, …
## $ RAVEN <dbl> 21, 22, 33, 23, 24, 31, 24, 27, 35, …
## $ Phonologic_Fluency <dbl> 19, 33, 38, 24, 26, 18, 41, 26, 43, …
## $ Semantic_Fluency <dbl> 12, 41, 31, 26, 40, 35, 40, 31, 36, …
## $ BNT <dbl> 51, 55, 49, 52, 50, 57, 50, 54, 57, …
## $ BNT_Sem <dbl> 3, 5, 3, 3, 7, 1, 4, 3, 2, 6, 2, 2, …
## $ BNT_NoResp <dbl> 3, 0, 6, 5, 2, 2, 4, 2, 1, 9, 0, 0, …
## $ SyllSec_Norm <dbl> 1.72, 2.06, 2.90, 2.53, 3.62, 2.45, …
## $ SyllSec_Ruis <dbl> 1.72, 2.23, 2.85, 2.29, 3.13, 2.42, …
## $ NPro_Dur_Norm <dbl> 28.5, 34.5, 30.0, 38.5, 26.5, 30.0, …
## $ NPro_Dur_Ruis <dbl> 30.5, 35.0, 33.0, 37.0, 29.5, 28.5, …
## $ NPro_NSyl_Norm <dbl> 48.5, 69.0, 87.0, 96.5, 96.0, 73.5, …
## $ NPro_NSyl_Ruis <dbl> 48.5, 69.0, 87.0, 96.5, 96.0, 73.5, …
## $ Rhym_Acc_Visual <dbl> 0.7833, 0.9667, 0.9167, 0.7167, 0.81…
## $ Rhym_RT_Visual <dbl> 4058.74, 4163.13, 1859.19, 3112.31, …
## $ ACC_HomTask <dbl> 0.7045, 0.9545, 0.6818, 0.6136, 0.88…
## $ ACC_NonHom <dbl> 0.6818, 0.9545, 0.4545, 0.5455, 0.90…
## $ ACC_Hom <dbl> 0.7273, 0.9545, 0.9091, 0.6818, 0.86…
## $ RT_NonHom <dbl> 5364, 3699, 2084, 4437, 2262, 10578,…
## $ RT_Hom <dbl> 3693, 2664, 2192, 3890, 1971, 7848, …
## $ Phon_Accuracy <dbl> 1.00, 0.96, 0.93, 0.80, 0.87, 0.96, …
## $ Phon_RT <dbl> 2835.37, 2096.66, 1709.49, 2322.79, …
## $ Rhym_Acc_Aud <dbl> 0.7667, 0.9818, 0.9667, 0.7333, 0.85…
## $ Rhym_RT_Auditory <dbl> 3996.33, 3434.89, 2626.97, 2556.05, …
## $ Ntw_Perc_Sem_NErrors <dbl> 7, 8, 10, 11, 7, 12, 16, 6, 14, 14, …
## $ Ntw_Perc_Detected <dbl> 7, 9, 10, 11, 7, 12, 18, 6, 15, 15, …
## $ ProductionSpeed <dbl> 1.720, 2.145, 2.875, 2.410, 3.375, 2…
## $ DurationDescription <dbl> 29.50, 34.75, 31.50, 37.75, 28.00, 2…
## $ Nrm_Completeness <dbl> 59.75, 84.28, 63.52, 67.30, 83.65, 8…
## $ Nrm_Reformulation <dbl> 2, 0, 2, 5, 5, 8, 2, 0, 12, 7, 0, 5,…
## $ Nrm_FilledPause <dbl> 4, 4, 0, 5, 0, 3, 10, 1, 4, 1, 0, 0,…
## $ Nrm_Repetition <dbl> 0, 0, 0, 0, 7, 4, 2, 0, 0, 0, 0, 1, …
## $ DisfluenciesNorm <dbl> 6, 4, 2, 10, 12, 15, 14, 1, 16, 8, 0…
## $ Nrm_Early_Repa <dbl> 3, 4, 3, 0, 1, 5, 2, 0, 2, 0, 2, 3, …
## $ Nrm_Sem_Unrep <dbl> 10, 1, 2, 5, 11, 0, 12, 6, 2, 3, 2, …
## $ Nrm_Sem_Rep <dbl> 3, 0, 4, 5, 3, 0, 0, 0, 0, 10, 1, 0,…
## $ SemErrors_Norm <dbl> 13, 1, 6, 10, 14, 0, 12, 6, 2, 13, 3…
## $ Nrm_Phon_Unrep <dbl> 0, 0, 3, 0, 2, 0, 0, 0, 0, 0, 0, 0, …
## $ Nrm_Phon_Rep <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, …
## $ PhonErrors_Norm <dbl> 0, 0, 3, 0, 2, 0, 0, 0, 0, 3, 0, 0, …
## $ Nrm_Gram_Unrep <dbl> 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, …
## $ Nrm_Gram_Rep <dbl> 0, 0, 4, 2, 0, 1, 1, 0, 1, 0, 0, 1, …
## $ GramErrors_Norm <dbl> 0, 0, 4, 3, 1, 2, 1, 0, 2, 0, 0, 1, …
## $ Norm_ErrorsUnrepaired <dbl> 10, 1, 5, 6, 14, 1, 12, 6, 3, 3, 2, …
## $ Norm_ErrorsRepaired <dbl> 12, 8, 13, 17, 16, 21, 17, 1, 19, 21…
## $ Norm_ErrorsRepaired_ExDisf <dbl> 6, 4, 11, 7, 4, 6, 3, 0, 3, 13, 3, 4…
## $ TotalErrorsNorm_N <dbl> 22, 9, 18, 23, 30, 22, 29, 7, 22, 24…
## $ TotalErrorsNormal_exDisf <dbl> 16, 5, 16, 13, 18, 7, 15, 6, 6, 16, …
## $ Nois_Completeness <dbl> 56.05, 85.35, 65.61, 64.97, 82.80, 8…
## $ Nois_Reformulation <dbl> 2, 0, 12, 3, 0, 1, 0, 0, 12, 11, 10,…
## $ Nois_FilledPause <dbl> 14, 1, 0, 6, 3, 2, 10, 2, 4, 1, 6, 1…
## $ Nois_Repetition <dbl> 11, 2, 0, 4, 18, 8, 6, 4, 0, 0, 1, 3…
## $ DisfluenciesNoise <dbl> 27, 3, 12, 13, 21, 11, 16, 6, 16, 12…
## $ Nois_Early_Rep <dbl> 2, 1, 2, 3, 0, 6, 2, 0, 1, 2, 1, 0, …
## $ Nois_Sem_Unrep <dbl> 8, 3, 4, 10, 4, 7, 0, 4, 3, 7, 0, 1,…
## $ Nois_Sem_Rep <dbl> 0, 0, 0, 0, 7, 4, 2, 1, 7, 6, 3, 0, …
## $ SemErrors_Noise <dbl> 8, 3, 4, 10, 11, 11, 2, 5, 10, 13, 3…
## $ Nois_Phon_Unrep <dbl> 2, 0, 0, 2, 1, 0, 1, 0, 0, 4, 0, 0, …
## $ Nois_Phon_Rep <dbl> 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, …
## $ PhonErrors_Nois <dbl> 2, 0, 0, 2, 1, 3, 1, 0, 0, 4, 0, 0, …
## $ Nois_Gram_Unrep <dbl> 1, 0, 0, 0, 4, 0, 0, 0, 1, 0, 0, 0, …
## $ Nois_Gram_Rep <dbl> 0, 0, 0, 0, 1, 1, 0, 0, 2, 0, 0, 0, …
## $ GramErrors_Nois <dbl> 1, 0, 0, 0, 5, 1, 0, 0, 3, 0, 0, 0, …
## $ Noise_ErrorsUnrepaired <dbl> 11, 3, 4, 12, 9, 7, 1, 4, 4, 11, 0, …
## $ Noise_ErrorsRepaired <dbl> 29, 4, 14, 16, 29, 25, 20, 7, 26, 20…
## $ Noise_ErrorsRepaired_ExDisf <dbl> 2, 1, 2, 3, 8, 14, 4, 1, 10, 8, 4, 0…
## $ TotalErrorsNois_N <dbl> 40, 7, 18, 28, 38, 32, 21, 11, 30, 3…
## $ TotalErrorsNoise_exDisf <dbl> 13, 4, 6, 15, 17, 21, 5, 5, 14, 19, …
## $ Perc_Norm_Dis <dbl> 27.27273, 44.44444, 11.11111, 43.478…
## $ Pct_NormRep <dbl> 54.54545, 88.88889, 72.22222, 73.913…
## $ Pct_NoiseRep <dbl> 72.50000, 57.14286, 77.77778, 57.142…
## $ PctSemRep <dbl> 0.14285714, 0.00000000, 0.40000000, …
## $ PctSemNoiseRep <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0…
## $ PctSemNormRep <dbl> 0.2307692, 0.0000000, 0.6666667, 0.5…
## $ PctPhonNormRep <dbl> NA, NA, 0.00, NA, 0.00, NA, NA, NA, …
## $ PctPhonNoisRep <dbl> 0, NA, NA, 0, 0, 1, 0, NA, NA, 0, NA…
## $ PctGraNormRep <dbl> NA, NA, 1.0000000, 0.6666667, 0.0000…
## $ PctGraNoiseRep <dbl> 0.0000000, NA, NA, NA, 0.2000000, 1.…
## $ SemanticWeight <dbl> 0.09910, 0.02475, 0.09789, 0.09222, …
## $ PhonologicalWeight <dbl> 0.01526, 0.03660, 0.01019, 0.03889, …
## $ `filter_$` <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
glimpse(allDataPDGroupsStudyDS)
## Observations: 18
## Variables: 92
## $ Subject <dbl> 504, 505, 506, 507, 508, 510, 511, 5…
## $ Group <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ Sex <chr+lbl> 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0,…
## $ Age <dbl> 69, 44, 62, 75, 73, 70, 60, 65, 65, …
## $ Length_Parkinson <dbl> 24, 20, 12, 20, 3, 6, 6, 10, 11, 9, …
## $ Medication <dbl> 600, 300, 500, 500, 800, 600, 200, 1…
## $ H_Y <dbl> NA, NA, 3.0, 3.0, 4.0, 2.0, NA, 2.0,…
## $ STN_Stimulation <dbl> 1, 1, 1, 0, NA, 0, 0, 0, 0, 0, 0, 0,…
## $ Handedness <dbl> 100.0, 100.0, 100.0, 95.8, 100.0, 91…
## $ MMSE <dbl> 23, 30, 24, 25, 26, 27, 29, 25, 30, …
## $ RAVEN <dbl> 21, 22, 33, 23, 24, 31, 24, 27, 35, …
## $ Phonologic_Fluency <dbl> 19, 33, 38, 24, 26, 18, 41, 26, 43, …
## $ Semantic_Fluency <dbl> 12, 41, 31, 26, 40, 35, 40, 31, 36, …
## $ BNT <dbl> 51, 55, 49, 52, 50, 57, 50, 54, 57, …
## $ BNT_Sem <dbl> 3, 5, 3, 3, 7, 1, 4, 3, 2, 6, 2, 2, …
## $ BNT_NoResp <dbl> 3, 0, 6, 5, 2, 2, 4, 2, 1, 9, 0, 0, …
## $ SyllSec_Norm <dbl> 1.72, 2.06, 2.90, 2.53, 3.62, 2.45, …
## $ SyllSec_Ruis <dbl> 1.72, 2.23, 2.85, 2.29, 3.13, 2.42, …
## $ NPro_Dur_Norm <dbl> 28.5, 34.5, 30.0, 38.5, 26.5, 30.0, …
## $ NPro_Dur_Ruis <dbl> 30.5, 35.0, 33.0, 37.0, 29.5, 28.5, …
## $ NPro_NSyl_Norm <dbl> 48.5, 69.0, 87.0, 96.5, 96.0, 73.5, …
## $ NPro_NSyl_Ruis <dbl> 48.5, 69.0, 87.0, 96.5, 96.0, 73.5, …
## $ Rhym_Acc_Visual <dbl> 0.7833, 0.9667, 0.9167, 0.7167, 0.81…
## $ Rhym_RT_Visual <dbl> 4058.74, 4163.13, 1859.19, 3112.31, …
## $ ACC_HomTask <dbl> 0.7045, 0.9545, 0.6818, 0.6136, 0.88…
## $ ACC_NonHom <dbl> 0.6818, 0.9545, 0.4545, 0.5455, 0.90…
## $ ACC_Hom <dbl> 0.7273, 0.9545, 0.9091, 0.6818, 0.86…
## $ RT_NonHom <dbl> 5364, 3699, 2084, 4437, 2262, 10578,…
## $ RT_Hom <dbl> 3693, 2664, 2192, 3890, 1971, 7848, …
## $ Phon_Accuracy <dbl> 1.00, 0.96, 0.93, 0.80, 0.87, 0.96, …
## $ Phon_RT <dbl> 2835.37, 2096.66, 1709.49, 2322.79, …
## $ Rhym_Acc_Aud <dbl> 0.7667, 0.9818, 0.9667, 0.7333, 0.85…
## $ Rhym_RT_Auditory <dbl> 3996.33, 3434.89, 2626.97, 2556.05, …
## $ Ntw_Perc_Sem_NErrors <dbl> 7, 8, 10, 11, 7, 12, 16, 6, 14, 14, …
## $ Ntw_Perc_Detected <dbl> 7, 9, 10, 11, 7, 12, 18, 6, 15, 15, …
## $ ProductionSpeed <dbl> 1.720, 2.145, 2.875, 2.410, 3.375, 2…
## $ DurationDescription <dbl> 29.50, 34.75, 31.50, 37.75, 28.00, 2…
## $ Nrm_Completeness <dbl> 59.75, 84.28, 63.52, 67.30, 83.65, 8…
## $ Nrm_Reformulation <dbl> 2, 0, 2, 5, 5, 8, 2, 0, 12, 7, 0, 5,…
## $ Nrm_FilledPause <dbl> 4, 4, 0, 5, 0, 3, 10, 1, 4, 1, 0, 0,…
## $ Nrm_Repetition <dbl> 0, 0, 0, 0, 7, 4, 2, 0, 0, 0, 0, 1, …
## $ DisfluenciesNorm <dbl> 6, 4, 2, 10, 12, 15, 14, 1, 16, 8, 0…
## $ Nrm_Early_Repa <dbl> 3, 4, 3, 0, 1, 5, 2, 0, 2, 0, 2, 3, …
## $ Nrm_Sem_Unrep <dbl> 10, 1, 2, 5, 11, 0, 12, 6, 2, 3, 2, …
## $ Nrm_Sem_Rep <dbl> 3, 0, 4, 5, 3, 0, 0, 0, 0, 10, 1, 0,…
## $ SemErrors_Norm <dbl> 13, 1, 6, 10, 14, 0, 12, 6, 2, 13, 3…
## $ Nrm_Phon_Unrep <dbl> 0, 0, 3, 0, 2, 0, 0, 0, 0, 0, 0, 0, …
## $ Nrm_Phon_Rep <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, …
## $ PhonErrors_Norm <dbl> 0, 0, 3, 0, 2, 0, 0, 0, 0, 3, 0, 0, …
## $ Nrm_Gram_Unrep <dbl> 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, …
## $ Nrm_Gram_Rep <dbl> 0, 0, 4, 2, 0, 1, 1, 0, 1, 0, 0, 1, …
## $ GramErrors_Norm <dbl> 0, 0, 4, 3, 1, 2, 1, 0, 2, 0, 0, 1, …
## $ Norm_ErrorsUnrepaired <dbl> 10, 1, 5, 6, 14, 1, 12, 6, 3, 3, 2, …
## $ Norm_ErrorsRepaired_ExDisf <dbl> 6, 4, 11, 7, 4, 6, 3, 0, 3, 13, 3, 4…
## $ TotalErrorsNormal_exDisf <dbl> 16, 5, 16, 13, 18, 7, 15, 6, 6, 16, …
## $ Nois_Completeness <dbl> 56.05, 85.35, 65.61, 64.97, 82.80, 8…
## $ Nois_Reformulation <dbl> 2, 0, 12, 3, 0, 1, 0, 0, 12, 11, 10,…
## $ Nois_FilledPause <dbl> 14, 1, 0, 6, 3, 2, 10, 2, 4, 1, 6, 1…
## $ Nois_Repetition <dbl> 11, 2, 0, 4, 18, 8, 6, 4, 0, 0, 1, 3…
## $ DisfluenciesNoise <dbl> 27, 3, 12, 13, 21, 11, 16, 6, 16, 12…
## $ Nois_Early_Rep <dbl> 2, 1, 2, 3, 0, 6, 2, 0, 1, 2, 1, 0, …
## $ Nois_Sem_Unrep <dbl> 8, 3, 4, 10, 4, 7, 0, 4, 3, 7, 0, 1,…
## $ Nois_Sem_Rep <dbl> 0, 0, 0, 0, 7, 4, 2, 1, 7, 6, 3, 0, …
## $ SemErrors_Noise <dbl> 8, 3, 4, 10, 11, 11, 2, 5, 10, 13, 3…
## $ Nois_Phon_Unrep <dbl> 2, 0, 0, 2, 1, 0, 1, 0, 0, 4, 0, 0, …
## $ Nois_Phon_Rep <dbl> 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, …
## $ PhonErrors_Nois <dbl> 2, 0, 0, 2, 1, 3, 1, 0, 0, 4, 0, 0, …
## $ Nois_Gram_Unrep <dbl> 1, 0, 0, 0, 4, 0, 0, 0, 1, 0, 0, 0, …
## $ Nois_Gram_Rep <dbl> 0, 0, 0, 0, 1, 1, 0, 0, 2, 0, 0, 0, …
## $ GramErrors_Nois <dbl> 1, 0, 0, 0, 5, 1, 0, 0, 3, 0, 0, 0, …
## $ Noise_ErrorsUnrepaired <dbl> 11, 3, 4, 12, 9, 7, 1, 4, 4, 11, 0, …
## $ Noise_ErrorsRepaired_ExDisf <dbl> 2, 1, 2, 3, 8, 14, 4, 1, 10, 8, 4, 0…
## $ TotalErrorsNoise_exDisf <dbl> 13, 4, 6, 15, 17, 21, 5, 5, 14, 19, …
## $ TotalErrorsNorm_N <dbl> 22, 9, 18, 23, 30, 22, 29, 7, 22, 24…
## $ Norm_ErrorsRepaired <dbl> 12, 8, 13, 17, 16, 21, 17, 1, 19, 21…
## $ TotalErrorsNois_N <dbl> 40, 7, 18, 28, 38, 32, 21, 11, 30, 3…
## $ Noise_ErrorsRepaired <dbl> 29, 4, 14, 16, 29, 25, 20, 7, 26, 20…
## $ Perc_Norm_Dis <dbl> 27.27273, 44.44444, 11.11111, 43.478…
## $ Pct_NormRep <dbl> 54.54545, 88.88889, 72.22222, 73.913…
## $ Pct_NoiseRep <dbl> 72.50000, 57.14286, 77.77778, 57.142…
## $ PctSemRep <dbl> 0.14285714, 0.00000000, 0.40000000, …
## $ PctSemNoiseRep <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0…
## $ PctSemNormRep <dbl> 0.2307692, 0.0000000, 0.6666667, 0.5…
## $ PctPhonNormRep <dbl> NA, NA, 0, NA, 0, NA, NA, NA, NA, 1,…
## $ PctPhonNoisRep <dbl> 0, NA, NA, 0, 0, 1, 0, NA, NA, 0, NA…
## $ PctGraNormRep <dbl> NA, NA, 1.0000000, 0.6666667, 0.0000…
## $ PctGraNoiseRep <dbl> 0.0000000, NA, NA, NA, 0.2000000, 1.…
## $ SemanticWeight <dbl> 0.09910, 0.02475, 0.09789, 0.09222, …
## $ PhonologicalWeight <dbl> 0.01526, 0.03660, 0.01019, 0.03889, …
## $ `filter_$` <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ Completeness <dbl> 57.900, 84.815, 64.565, 66.135, 83.2…
## $ Repetitions <dbl> 11, 2, 0, 4, 25, 12, 8, 4, 0, 0, 1, …
variable.names(allDataPatientStudyDS)
## [1] "Subject" "Group"
## [3] "Sex" "Age"
## [5] "Length_Parkinson" "Medication"
## [7] "H_Y" "STN_Stimulation"
## [9] "Handedness" "MMSE"
## [11] "RAVEN" "Phonologic_Fluency"
## [13] "Semantic_Fluency" "BNT"
## [15] "BNT_Sem" "BNT_NoResp"
## [17] "SyllSec_Norm" "SyllSec_Ruis"
## [19] "NPro_Dur_Norm" "NPro_Dur_Ruis"
## [21] "NPro_NSyl_Norm" "NPro_NSyl_Ruis"
## [23] "Rhym_Acc_Visual" "Rhym_RT_Visual"
## [25] "ACC_HomTask" "ACC_NonHom"
## [27] "ACC_Hom" "RT_NonHom"
## [29] "RT_Hom" "Phon_Accuracy"
## [31] "Phon_RT" "Rhym_Acc_Aud"
## [33] "Rhym_RT_Auditory" "Ntw_Perc_Sem_NErrors"
## [35] "Ntw_Perc_Detected" "ProductionSpeed"
## [37] "DurationDescription" "Nrm_Completeness"
## [39] "Nrm_Reformulation" "Nrm_FilledPause"
## [41] "Nrm_Repetition" "DisfluenciesNorm"
## [43] "Nrm_Early_Repa" "Nrm_Sem_Unrep"
## [45] "Nrm_Sem_Rep" "SemErrors_Norm"
## [47] "Nrm_Phon_Unrep" "Nrm_Phon_Rep"
## [49] "PhonErrors_Norm" "Nrm_Gram_Unrep"
## [51] "Nrm_Gram_Rep" "GramErrors_Norm"
## [53] "Norm_ErrorsUnrepaired" "Norm_ErrorsRepaired"
## [55] "Norm_ErrorsRepaired_ExDisf" "TotalErrorsNorm_N"
## [57] "TotalErrorsNormal_exDisf" "Nois_Completeness"
## [59] "Nois_Reformulation" "Nois_FilledPause"
## [61] "Nois_Repetition" "DisfluenciesNoise"
## [63] "Nois_Early_Rep" "Nois_Sem_Unrep"
## [65] "Nois_Sem_Rep" "SemErrors_Noise"
## [67] "Nois_Phon_Unrep" "Nois_Phon_Rep"
## [69] "PhonErrors_Nois" "Nois_Gram_Unrep"
## [71] "Nois_Gram_Rep" "GramErrors_Nois"
## [73] "Noise_ErrorsUnrepaired" "Noise_ErrorsRepaired"
## [75] "Noise_ErrorsRepaired_ExDisf" "TotalErrorsNois_N"
## [77] "TotalErrorsNoise_exDisf" "Perc_Norm_Dis"
## [79] "Pct_NormRep" "Pct_NoiseRep"
## [81] "PctSemRep" "PctSemNoiseRep"
## [83] "PctSemNormRep" "PctPhonNormRep"
## [85] "PctPhonNoisRep" "PctGraNormRep"
## [87] "PctGraNoiseRep" "SemanticWeight"
## [89] "PhonologicalWeight" "filter_$"
library(codebook)
library(igraph)
Age - age of participant in whole years Length_Parkinson - duration of disease in years for each patient Medication - Levodopa equivalent, rounded to nearest 50 H_Y - Hoehn Yahr Sex - biological sex of patient Handedness - whether the patient is right or left handed MMSE - mini mental state examination RAVEN - Raven’s Progressive Matrices Phonologic_Fluency - interger representation of a component a verbal fuency test Semantic_Fluency - interger representation of a component a verbal fuency test BNT - Boston Naming Test SyllSec_Norm - syllables per second, normal SyllSec_Ruis - to do NPro_Dur_Norm - duration of network task, nearest half-integer NPro_Dur_Ruis - to do Nrm_Reformulation - repair without initial production of error Nrm_FilledPause - prolongation such as “uh” Nrm_Repetition - repetition of same words without adjustment DisfluenciesNorm - an irregularity, break, or non-lexical pattern of speech
Nois_Reformulation - repair under noise masking Nois_FilledPause - prolongations under noise masking Nois_Repetition - repitition under noise masking DisfluenciesNoise - disfluency under noise masking Nrm_Sem_Unrep - normal semantic unrepaired errors Nrm_Sem_Rep - normal semantic repaird errors SemErrors_Norm - normal semantic errors Nrm_Phon_Unrep - normal phonetic unrepaired errors Nrm_Phon_Rep - normal phonetic repaired errors PhonErrors_Norm - normal phonetic errors Nrm_Gram_Unrep - normal grammatical unrepaired errors Nrm_Gram_Rep - normal grammatical repaired errors GramErrors_Norm - normal grammatical errors Norm_ErrorsUnrepaired - normal errors unrepaired Norm_ErrorsRepaired - normal errors repaired TotalErrorsNorm_N - total normal errors Nois_Sem_Unrep - normal semantic noise Nois_Sem_Rep - normal phonetic noise SemErrors_Noise - noise semantic errors Nois_Phon_Unrep - phonetic unrepaired errors noise Nois_Phon_Rep - phonetic repaired errors noise PhonErrors_Nois - phonetic errors noise Nois_Gram_Unrep - grammatical unrepaired errors noise Nois_Gram_Rep - grammatical repaired errors noise GramErrors_Nois - grammatical errors noise Noise_ErrorsUnrepaired - errors repaired noise Noise_ErrorsRepaired - errors unrepaired noise TotalErrorsNois_N - total errors noise Norm_ErrorsRepaired_ExDisf - normal errrors repaired, excluding disfluencies (?) TotalErrorsNormal_exDisf - total normal errors, excluding disfluencies Noise_ErrorsRepaired_ExDisf - repaired noise errors, excluding disfluencies TotalErrorsNoise_exDisf - total noise errors, excluding disfluencies
patientStudy <- allDataPatientStudyDS %>%
select("Age", "Length_Parkinson", "Medication", "H_Y","Sex", "Handedness", "MMSE","RAVEN", "Phonologic_Fluency", "Semantic_Fluency", "BNT", "SyllSec_Norm", "SyllSec_Ruis", "NPro_Dur_Norm", "NPro_Dur_Ruis", "Nrm_Reformulation", "Nrm_FilledPause", "Nrm_Repetition", "DisfluenciesNorm", "Nois_Reformulation", "Nois_FilledPause", "Nois_Repetition", "DisfluenciesNoise", "Nrm_Sem_Unrep", "Nrm_Sem_Rep", "SemErrors_Norm", "Nrm_Phon_Unrep", "Nrm_Phon_Rep", "PhonErrors_Norm", "Nrm_Gram_Unrep", "Nrm_Gram_Rep", "GramErrors_Norm", "Norm_ErrorsUnrepaired", "Norm_ErrorsRepaired", "TotalErrorsNorm_N", "Nois_Sem_Unrep", "Nois_Sem_Rep", "SemErrors_Noise", "Nois_Phon_Unrep", "Nois_Phon_Rep", "PhonErrors_Nois", "Nois_Gram_Unrep", "Nois_Gram_Rep", "GramErrors_Nois", "Noise_ErrorsUnrepaired", "Noise_ErrorsRepaired", "TotalErrorsNois_N", "Norm_ErrorsRepaired_ExDisf", "TotalErrorsNormal_exDisf", "Noise_ErrorsRepaired_ExDisf", "TotalErrorsNoise_exDisf")
groupStudy <- allDataPDGroupsStudyDS %>%
select("Age", "Medication", "Length_Parkinson","H_Y", "Sex", "Handedness", "MMSE", "RAVEN", "Phonologic_Fluency", "BNT", "SyllSec_Norm", "SyllSec_Ruis", "NPro_Dur_Norm", "NPro_Dur_Ruis", "Nrm_Reformulation", "Nrm_FilledPause", "Nrm_Repetition", "DisfluenciesNorm", "Nois_Reformulation", "Nois_FilledPause", "Nois_Repetition", "DisfluenciesNoise", "Nrm_Sem_Unrep", "Nrm_Sem_Rep", "SemErrors_Norm", "Nrm_Phon_Unrep", "Nrm_Phon_Rep", "PhonErrors_Norm", "Nrm_Gram_Unrep", "Nrm_Gram_Rep", "GramErrors_Norm", "Norm_ErrorsUnrepaired", "Norm_ErrorsRepaired", "TotalErrorsNorm_N", "Nois_Sem_Unrep", "Nois_Sem_Rep", "SemErrors_Noise", "Nois_Phon_Unrep", "Nois_Phon_Rep", "PhonErrors_Nois", "Nois_Gram_Unrep", "Nois_Gram_Rep", "GramErrors_Nois", "Noise_ErrorsUnrepaired", "Noise_ErrorsRepaired", "TotalErrorsNois_N", "Norm_ErrorsRepaired_ExDisf", "TotalErrorsNormal_exDisf", "Noise_ErrorsRepaired_ExDisf", "TotalErrorsNoise_exDisf")
codebook(patientStudy)
knitr::asis_output(data_info)
if (exists("name", meta)) {
glue::glue(
"__Dataset name__: {name}",
.envir = meta)
}
Dataset name: patientStudy
cat(description)
The dataset has N=34 rows and 51 columns. 31 rows have no missing values on any column.
Metadata for search engines
meta <- meta[setdiff(names(meta),
c("creator", "datePublished", "identifier",
"url", "citation", "spatialCoverage",
"temporalCoverage", "description", "name"))]
pander::pander(meta)
knitr::asis_output(survey_overview)
if (detailed_variables || detailed_scales) {
knitr::asis_output(paste0(scales_items, sep = "\n\n\n", collapse = "\n\n\n"))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Age | numeric | 0 | 34 | 34 | 65 | 8.31 | 43 | 62 | 66.5 | 69 | 82 | ▂▁▁▆▇▇▂▂ | F8.0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Length_Parkinson | numeric | 0 | 34 | 34 | 5.56 | 6.89 | 0 | 0 | 3 | 9.75 | 24 | ▇▂▁▁▁▁▁▁ | F8.0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Medication | numeric | 0 | 34 | 34 | 344.12 | 392.88 | 0 | 0 | 225 | 600 | 1400 | ▇▂▂▃▁▂▁▁ | F8.0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
3 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| H_Y | numeric | 3 | 31 | 34 | 1.16 | 1.36 | 0 | 0 | 0 | 2 | 4 | ▇▁▁▃▁▂▁▁ | F8.0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
0
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | value_labels | missing | complete | n | empty | n_unique | min | max | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|
| Sex | character | 0. male, 1. female |
0 | 34 | 34 | 0 | 2 | 1 | 1 | A8 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Handedness | numeric | 0 | 34 | 34 | 83.83 | 38.58 | -58.3 | 91.7 | 97.9 | 100 | 100 | ▁▁▁▁▁▁▁▇ | F8.2 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MMSE | numeric | 0 | 34 | 34 | 27.26 | 2.22 | 23 | 26 | 28 | 29 | 30 | ▃▂▃▃▇▇▇▇ | F8.0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| RAVEN | numeric | 0 | 34 | 34 | 26.44 | 4.84 | 15 | 23 | 25.5 | 30 | 35 | ▁▁▃▇▂▅▂▃ | F8.0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Phonologic_Fluency | numeric | 0 | 34 | 34 | 31.35 | 10.5 | 12 | 24.25 | 29 | 38.75 | 55 | ▁▃▇▃▂▃▂▂ | F18.0 | 12 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Semantic_Fluency | numeric | 0 | 34 | 34 | 32.32 | 8.57 | 12 | 27.25 | 31.5 | 36 | 59 | ▁▂▃▇▃▁▁▁ | F16.0 | 12 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BNT | numeric | 0 | 34 | 34 | 51.68 | 4.36 | 41 | 49.25 | 52 | 55 | 59 | ▂▁▃▇▇▆▇▅ | F8.0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SyllSec_Norm | numeric | 0 | 34 | 34 | 2.69 | 0.61 | 1.47 | 2.11 | 2.83 | 3.12 | 3.68 | ▃▁▇▃▃▇▃▃ | F8.2 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SyllSec_Ruis | numeric | 0 | 34 | 34 | 2.54 | 0.62 | 1.1 | 2.09 | 2.6 | 2.96 | 3.81 | ▂▃▃▆▅▇▅▁ | F8.2 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| NPro_Dur_Norm | numeric | 0 | 34 | 34 | 29.4 | 4.36 | 24.5 | 26.62 | 28 | 30 | 41 | ▆▇▃▁▁▁▁▂ | F8.2 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| NPro_Dur_Ruis | numeric | 0 | 34 | 34 | 30.47 | 4.19 | 26 | 28 | 29.25 | 30.5 | 42 | ▆▇▂▁▁▁▁▂ | F8.2 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Nrm_Reformulation | numeric | 0 | 34 | 34 | 3.82 | 3.28 | 0 | 1 | 3.5 | 5 | 12 | ▇▆▂▅▃▂▁▂ | F8.0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Nrm_FilledPause | numeric | 0 | 34 | 34 | 4.06 | 3.85 | 0 | 1 | 3.5 | 6 | 15 | ▇▆▆▃▂▁▁▂ | F8.0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Nrm_Repetition | numeric | 0 | 34 | 34 | 2.03 | 2.94 | 0 | 0 | 1 | 2.75 | 13 | ▇▂▁▁▁▁▁▁ | F8.0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| DisfluenciesNorm | numeric | 0 | 34 | 34 | 9.91 | 5.78 | 0 | 5.25 | 10.5 | 14.75 | 22 | ▅▆▇▅▅▇▃▁ | F8.2 | 14 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Nois_Reformulation | numeric | 0 | 34 | 34 | 3.44 | 3.73 | 0 | 0 | 2.5 | 5.75 | 12 | ▇▃▂▂▁▁▁▂ | F8.0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Nois_FilledPause | numeric | 0 | 34 | 34 | 3.88 | 3.47 | 0 | 1 | 2.5 | 6 | 14 | ▇▆▅▆▁▁▁▂ | F8.0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Nois_Repetition | numeric | 0 | 34 | 34 | 3.09 | 4.07 | 0 | 0 | 2 | 4 | 18 | ▇▃▁▁▁▁▁▁ | F8.0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| DisfluenciesNoise | numeric | 0 | 34 | 34 | 10.41 | 6.13 | 0 | 6 | 10 | 13.75 | 27 | ▃▆▇▇▃▃▁▁ | F8.2 | 19 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Nrm_Sem_Unrep | numeric | 0 | 34 | 34 | 3.59 | 4.32 | 0 | 0 | 2 | 6 | 14 | ▇▃▁▂▁▁▂▁ | F8.0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Nrm_Sem_Rep | numeric | 0 | 34 | 34 | 2.65 | 2.78 | 0 | 0 | 2 | 4 | 10 | ▇▂▂▃▁▁▁▁ | F8.0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SemErrors_Norm | numeric | 0 | 34 | 34 | 6.24 | 5.03 | 0 | 2 | 5.5 | 12 | 16 | ▇▅▃▂▁▃▃▁ | F8.2 | 16 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Nrm_Phon_Unrep | numeric | 0 | 34 | 34 | 0.32 | 0.84 | 0 | 0 | 0 | 0 | 3 | ▇▁▁▁▁▁▁▁ | F8.0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Nrm_Phon_Rep | numeric | 0 | 34 | 34 | 0.35 | 0.88 | 0 | 0 | 0 | 0 | 3 | ▇▁▁▁▁▁▁▁ | F8.0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PhonErrors_Norm | numeric | 0 | 34 | 34 | 0.68 | 1.25 | 0 | 0 | 0 | 0.75 | 4 | ▇▁▁▁▁▂▁▁ | F8.2 | 17 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Nrm_Gram_Unrep | numeric | 0 | 34 | 34 | 0.38 | 0.49 | 0 | 0 | 0 | 1 | 1 | ▇▁▁▁▁▁▁▅ | F8.0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Nrm_Gram_Rep | numeric | 0 | 34 | 34 | 0.59 | 0.96 | 0 | 0 | 0 | 1 | 4 | ▇▂▁▂▁▁▁▁ | F8.0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GramErrors_Norm | numeric | 0 | 34 | 34 | 0.97 | 1.11 | 0 | 0 | 1 | 1.75 | 4 | ▇▅▁▂▁▂▁▁ | F8.2 | 17 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Norm_ErrorsUnrepaired | numeric | 0 | 34 | 34 | 4.29 | 4.58 | 0 | 1 | 2 | 6 | 17 | ▇▂▂▁▁▁▁▁ | F8.2 | 17 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Norm_ErrorsRepaired | numeric | 0 | 34 | 34 | 15.97 | 7.43 | 1 | 9.25 | 17 | 20.5 | 31 | ▂▅▂▂▇▃▂▂ | F8.2 | 15 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TotalErrorsNorm_N | numeric | 0 | 34 | 34 | 20.26 | 8.76 | 5 | 15.5 | 20 | 26.25 | 43 | ▃▁▃▇▁▃▁▁ | F8.2 | 16 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Nois_Sem_Unrep | numeric | 0 | 34 | 34 | 4 | 3.03 | 0 | 2 | 3.5 | 6.75 | 10 | ▇▃▆▅▃▅▁▃ | F8.0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Nois_Sem_Rep | numeric | 0 | 34 | 34 | 1.94 | 2.35 | 0 | 0 | 1 | 3 | 7 | ▇▂▃▁▁▁▁▂ | F8.0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SemErrors_Noise | numeric | 0 | 34 | 34 | 5.94 | 4.18 | 0 | 3 | 5.5 | 8 | 16 | ▇▇▇▆▃▂▁▂ | F8.2 | 17 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Nois_Phon_Unrep | numeric | 0 | 34 | 34 | 0.59 | 1.16 | 0 | 0 | 0 | 0.75 | 4 | ▇▁▁▁▁▁▁▁ | F8.0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Nois_Phon_Rep | numeric | 0 | 34 | 34 | 0.12 | 0.54 | 0 | 0 | 0 | 0 | 3 | ▇▁▁▁▁▁▁▁ | F8.0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PhonErrors_Nois | numeric | 0 | 34 | 34 | 0.71 | 1.22 | 0 | 0 | 0 | 1 | 4 | ▇▂▁▁▁▁▁▁ | F8.2 | 17 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Nois_Gram_Unrep | numeric | 0 | 34 | 34 | 0.29 | 0.8 | 0 | 0 | 0 | 0 | 4 | ▇▁▁▁▁▁▁▁ | F8.0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Nois_Gram_Rep | numeric | 0 | 34 | 34 | 0.24 | 0.55 | 0 | 0 | 0 | 0 | 2 | ▇▁▁▁▁▁▁▁ | F8.0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GramErrors_Nois | numeric | 0 | 34 | 34 | 0.53 | 1.08 | 0 | 0 | 0 | 1 | 5 | ▇▂▁▁▁▁▁▁ | F8.2 | 17 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Noise_ErrorsUnrepaired | numeric | 0 | 34 | 34 | 4.88 | 3.59 | 0 | 2.25 | 4 | 7 | 12 | ▇▆▃▅▃▃▁▃ | F8.2 | 17 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Noise_ErrorsRepaired | numeric | 0 | 34 | 34 | 15.09 | 7.79 | 2 | 9.5 | 14.5 | 21 | 29 | ▅▅▇▇▃▅▇▃ | F8.2 | 18 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TotalErrorsNois_N | numeric | 0 | 34 | 34 | 19.97 | 9.8 | 2 | 12.5 | 19.5 | 26.75 | 40 | ▃▃▅▇▃▅▃▂ | F8.2 | 14 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Norm_ErrorsRepaired_ExDisf | numeric | 0 | 34 | 34 | 6.06 | 3.35 | 0 | 3.25 | 6 | 8 | 14 | ▁▇▇▇▂▃▃▂ | F8.2 | 15 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TotalErrorsNormal_exDisf | numeric | 0 | 34 | 34 | 10.35 | 5.27 | 2 | 6 | 10.5 | 14 | 25 | ▃▇▂▅▆▂▁▁ | F8.2 | 15 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Noise_ErrorsRepaired_ExDisf | numeric | 0 | 34 | 34 | 4.68 | 3.58 | 0 | 2 | 4 | 7.75 | 14 | ▅▇▇▁▃▂▁▁ | F8.2 | 22 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist | format.spss | display_width |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TotalErrorsNoise_exDisf | numeric | 0 | 34 | 34 | 9.56 | 5.84 | 1 | 4.25 | 8 | 13.75 | 21 | ▅▇▅▃▃▃▁▃ | F8.2 | 13 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
missingness_report
if (length(md_pattern)) {
if (knitr::is_html_output()) {
rmarkdown::paged_table(md_pattern, options = list(rows.print = 10))
} else {
knitr::kable(md_pattern)
}
}
items
export_table(metadata_table)
jsonld
JSON-LD metadata
The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.
{
"name": "patientStudy",
"datePublished": "2019-11-08",
"description": "The dataset has N=34 rows and 51 columns.\n31 rows have no missing values on any column.\n\n\n## Table of variables\nThis table contains variable names, labels, their central tendencies and other attributes.\n\n|name |data_type |value_labels |missing |complete |n |empty |n_unique |min |max |mean |sd |p0 |p25 |p50 |p75 |p100 |hist |format.spss |display_width |\n|:---------------------------|:---------|:------------------|:-------|:--------|:--|:-----|:--------|:---|:---|:------|:------|:-----|:-----|:-----|:-----|:----|:--------|:-----------|:-------------|\n|Age |numeric |NA |0 |34 |34 |NA |NA |NA |NA |65 |8.31 |43 |62 |66.5 |69 |82 |▂▁▁▆▇▇▂▂ |F8.0 |NA |\n|Length_Parkinson |numeric |NA |0 |34 |34 |NA |NA |NA |NA |5.56 |6.89 |0 |0 |3 |9.75 |24 |▇▂▁▁▁▁▁▁ |F8.0 |NA |\n|Medication |numeric |NA |0 |34 |34 |NA |NA |NA |NA |344.12 |392.88 |0 |0 |225 |600 |1400 |▇▂▂▃▁▂▁▁ |F8.0 |NA |\n|H_Y |numeric |NA |3 |31 |34 |NA |NA |NA |NA |1.16 |1.36 |0 |0 |0 |2 |4 |▇▁▁▃▁▂▁▁ |F8.0 |NA |\n|Sex |character |0. male, - 1. female |0 |34 |34 |0 |2 |1 |1 |NA |NA |NA |NA |NA |NA |NA |NA |A8 |NA |\n|Handedness |numeric |NA |0 |34 |34 |NA |NA |NA |NA |83.83 |38.58 |-58.3 |91.7 |97.9 |100 |100 |▁▁▁▁▁▁▁▇ |F8.2 |NA |\n|MMSE |numeric |NA |0 |34 |34 |NA |NA |NA |NA |27.26 |2.22 |23 |26 |28 |29 |30 |▃▂▃▃▇▇▇▇ |F8.0 |NA |\n|RAVEN |numeric |NA |0 |34 |34 |NA |NA |NA |NA |26.44 |4.84 |15 |23 |25.5 |30 |35 |▁▁▃▇▂▅▂▃ |F8.0 |NA |\n|Phonologic_Fluency |numeric |NA |0 |34 |34 |NA |NA |NA |NA |31.35 |10.5 |12 |24.25 |29 |38.75 |55 |▁▃▇▃▂▃▂▂ |F18.0 |12 |\n|Semantic_Fluency |numeric |NA |0 |34 |34 |NA |NA |NA |NA |32.32 |8.57 |12 |27.25 |31.5 |36 |59 |▁▂▃▇▃▁▁▁ |F16.0 |12 |\n|BNT |numeric |NA |0 |34 |34 |NA |NA |NA |NA |51.68 |4.36 |41 |49.25 |52 |55 |59 |▂▁▃▇▇▆▇▅ |F8.0 |NA |\n|SyllSec_Norm |numeric |NA |0 |34 |34 |NA |NA |NA |NA |2.69 |0.61 |1.47 |2.11 |2.83 |3.12 |3.68 |▃▁▇▃▃▇▃▃ |F8.2 |NA |\n|SyllSec_Ruis |numeric |NA |0 |34 |34 |NA |NA |NA |NA |2.54 |0.62 |1.1 |2.09 |2.6 |2.96 |3.81 |▂▃▃▆▅▇▅▁ |F8.2 |NA |\n|NPro_Dur_Norm |numeric |NA |0 |34 |34 |NA |NA |NA |NA |29.4 |4.36 |24.5 |26.62 |28 |30 |41 |▆▇▃▁▁▁▁▂ |F8.2 |NA |\n|NPro_Dur_Ruis |numeric |NA |0 |34 |34 |NA |NA |NA |NA |30.47 |4.19 |26 |28 |29.25 |30.5 |42 |▆▇▂▁▁▁▁▂ |F8.2 |NA |\n|Nrm_Reformulation |numeric |NA |0 |34 |34 |NA |NA |NA |NA |3.82 |3.28 |0 |1 |3.5 |5 |12 |▇▆▂▅▃▂▁▂ |F8.0 |NA |\n|Nrm_FilledPause |numeric |NA |0 |34 |34 |NA |NA |NA |NA |4.06 |3.85 |0 |1 |3.5 |6 |15 |▇▆▆▃▂▁▁▂ |F8.0 |NA |\n|Nrm_Repetition |numeric |NA |0 |34 |34 |NA |NA |NA |NA |2.03 |2.94 |0 |0 |1 |2.75 |13 |▇▂▁▁▁▁▁▁ |F8.0 |NA |\n|DisfluenciesNorm |numeric |NA |0 |34 |34 |NA |NA |NA |NA |9.91 |5.78 |0 |5.25 |10.5 |14.75 |22 |▅▆▇▅▅▇▃▁ |F8.2 |14 |\n|Nois_Reformulation |numeric |NA |0 |34 |34 |NA |NA |NA |NA |3.44 |3.73 |0 |0 |2.5 |5.75 |12 |▇▃▂▂▁▁▁▂ |F8.0 |NA |\n|Nois_FilledPause |numeric |NA |0 |34 |34 |NA |NA |NA |NA |3.88 |3.47 |0 |1 |2.5 |6 |14 |▇▆▅▆▁▁▁▂ |F8.0 |NA |\n|Nois_Repetition |numeric |NA |0 |34 |34 |NA |NA |NA |NA |3.09 |4.07 |0 |0 |2 |4 |18 |▇▃▁▁▁▁▁▁ |F8.0 |NA |\n|DisfluenciesNoise |numeric |NA |0 |34 |34 |NA |NA |NA |NA |10.41 |6.13 |0 |6 |10 |13.75 |27 |▃▆▇▇▃▃▁▁ |F8.2 |19 |\n|Nrm_Sem_Unrep |numeric |NA |0 |34 |34 |NA |NA |NA |NA |3.59 |4.32 |0 |0 |2 |6 |14 |▇▃▁▂▁▁▂▁ |F8.0 |NA |\n|Nrm_Sem_Rep |numeric |NA |0 |34 |34 |NA |NA |NA |NA |2.65 |2.78 |0 |0 |2 |4 |10 |▇▂▂▃▁▁▁▁ |F8.0 |NA |\n|SemErrors_Norm |numeric |NA |0 |34 |34 |NA |NA |NA |NA |6.24 |5.03 |0 |2 |5.5 |12 |16 |▇▅▃▂▁▃▃▁ |F8.2 |16 |\n|Nrm_Phon_Unrep |numeric |NA |0 |34 |34 |NA |NA |NA |NA |0.32 |0.84 |0 |0 |0 |0 |3 |▇▁▁▁▁▁▁▁ |F8.0 |NA |\n|Nrm_Phon_Rep |numeric |NA |0 |34 |34 |NA |NA |NA |NA |0.35 |0.88 |0 |0 |0 |0 |3 |▇▁▁▁▁▁▁▁ |F8.0 |NA |\n|PhonErrors_Norm |numeric |NA |0 |34 |34 |NA |NA |NA |NA |0.68 |1.25 |0 |0 |0 |0.75 |4 |▇▁▁▁▁▂▁▁ |F8.2 |17 |\n|Nrm_Gram_Unrep |numeric |NA |0 |34 |34 |NA |NA |NA |NA |0.38 |0.49 |0 |0 |0 |1 |1 |▇▁▁▁▁▁▁▅ |F8.0 |NA |\n|Nrm_Gram_Rep |numeric |NA |0 |34 |34 |NA |NA |NA |NA |0.59 |0.96 |0 |0 |0 |1 |4 |▇▂▁▂▁▁▁▁ |F8.0 |NA |\n|GramErrors_Norm |numeric |NA |0 |34 |34 |NA |NA |NA |NA |0.97 |1.11 |0 |0 |1 |1.75 |4 |▇▅▁▂▁▂▁▁ |F8.2 |17 |\n|Norm_ErrorsUnrepaired |numeric |NA |0 |34 |34 |NA |NA |NA |NA |4.29 |4.58 |0 |1 |2 |6 |17 |▇▂▂▁▁▁▁▁ |F8.2 |17 |\n|Norm_ErrorsRepaired |numeric |NA |0 |34 |34 |NA |NA |NA |NA |15.97 |7.43 |1 |9.25 |17 |20.5 |31 |▂▅▂▂▇▃▂▂ |F8.2 |15 |\n|TotalErrorsNorm_N |numeric |NA |0 |34 |34 |NA |NA |NA |NA |20.26 |8.76 |5 |15.5 |20 |26.25 |43 |▃▁▃▇▁▃▁▁ |F8.2 |16 |\n|Nois_Sem_Unrep |numeric |NA |0 |34 |34 |NA |NA |NA |NA |4 |3.03 |0 |2 |3.5 |6.75 |10 |▇▃▆▅▃▅▁▃ |F8.0 |NA |\n|Nois_Sem_Rep |numeric |NA |0 |34 |34 |NA |NA |NA |NA |1.94 |2.35 |0 |0 |1 |3 |7 |▇▂▃▁▁▁▁▂ |F8.0 |NA |\n|SemErrors_Noise |numeric |NA |0 |34 |34 |NA |NA |NA |NA |5.94 |4.18 |0 |3 |5.5 |8 |16 |▇▇▇▆▃▂▁▂ |F8.2 |17 |\n|Nois_Phon_Unrep |numeric |NA |0 |34 |34 |NA |NA |NA |NA |0.59 |1.16 |0 |0 |0 |0.75 |4 |▇▁▁▁▁▁▁▁ |F8.0 |NA |\n|Nois_Phon_Rep |numeric |NA |0 |34 |34 |NA |NA |NA |NA |0.12 |0.54 |0 |0 |0 |0 |3 |▇▁▁▁▁▁▁▁ |F8.0 |NA |\n|PhonErrors_Nois |numeric |NA |0 |34 |34 |NA |NA |NA |NA |0.71 |1.22 |0 |0 |0 |1 |4 |▇▂▁▁▁▁▁▁ |F8.2 |17 |\n|Nois_Gram_Unrep |numeric |NA |0 |34 |34 |NA |NA |NA |NA |0.29 |0.8 |0 |0 |0 |0 |4 |▇▁▁▁▁▁▁▁ |F8.0 |NA |\n|Nois_Gram_Rep |numeric |NA |0 |34 |34 |NA |NA |NA |NA |0.24 |0.55 |0 |0 |0 |0 |2 |▇▁▁▁▁▁▁▁ |F8.0 |NA |\n|GramErrors_Nois |numeric |NA |0 |34 |34 |NA |NA |NA |NA |0.53 |1.08 |0 |0 |0 |1 |5 |▇▂▁▁▁▁▁▁ |F8.2 |17 |\n|Noise_ErrorsUnrepaired |numeric |NA |0 |34 |34 |NA |NA |NA |NA |4.88 |3.59 |0 |2.25 |4 |7 |12 |▇▆▃▅▃▃▁▃ |F8.2 |17 |\n|Noise_ErrorsRepaired |numeric |NA |0 |34 |34 |NA |NA |NA |NA |15.09 |7.79 |2 |9.5 |14.5 |21 |29 |▅▅▇▇▃▅▇▃ |F8.2 |18 |\n|TotalErrorsNois_N |numeric |NA |0 |34 |34 |NA |NA |NA |NA |19.97 |9.8 |2 |12.5 |19.5 |26.75 |40 |▃▃▅▇▃▅▃▂ |F8.2 |14 |\n|Norm_ErrorsRepaired_ExDisf |numeric |NA |0 |34 |34 |NA |NA |NA |NA |6.06 |3.35 |0 |3.25 |6 |8 |14 |▁▇▇▇▂▃▃▂ |F8.2 |15 |\n|TotalErrorsNormal_exDisf |numeric |NA |0 |34 |34 |NA |NA |NA |NA |10.35 |5.27 |2 |6 |10.5 |14 |25 |▃▇▂▅▆▂▁▁ |F8.2 |15 |\n|Noise_ErrorsRepaired_ExDisf |numeric |NA |0 |34 |34 |NA |NA |NA |NA |4.68 |3.58 |0 |2 |4 |7.75 |14 |▅▇▇▁▃▂▁▁ |F8.2 |22 |\n|TotalErrorsNoise_exDisf |numeric |NA |0 |34 |34 |NA |NA |NA |NA |9.56 |5.84 |1 |4.25 |8 |13.75 |21 |▅▇▅▃▃▃▁▃ |F8.2 |13 |\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.8.1).",
"keywords": ["Age", "Length_Parkinson", "Medication", "H_Y", "Sex", "Handedness", "MMSE", "RAVEN", "Phonologic_Fluency", "Semantic_Fluency", "BNT", "SyllSec_Norm", "SyllSec_Ruis", "NPro_Dur_Norm", "NPro_Dur_Ruis", "Nrm_Reformulation", "Nrm_FilledPause", "Nrm_Repetition", "DisfluenciesNorm", "Nois_Reformulation", "Nois_FilledPause", "Nois_Repetition", "DisfluenciesNoise", "Nrm_Sem_Unrep", "Nrm_Sem_Rep", "SemErrors_Norm", "Nrm_Phon_Unrep", "Nrm_Phon_Rep", "PhonErrors_Norm", "Nrm_Gram_Unrep", "Nrm_Gram_Rep", "GramErrors_Norm", "Norm_ErrorsUnrepaired", "Norm_ErrorsRepaired", "TotalErrorsNorm_N", "Nois_Sem_Unrep", "Nois_Sem_Rep", "SemErrors_Noise", "Nois_Phon_Unrep", "Nois_Phon_Rep", "PhonErrors_Nois", "Nois_Gram_Unrep", "Nois_Gram_Rep", "GramErrors_Nois", "Noise_ErrorsUnrepaired", "Noise_ErrorsRepaired", "TotalErrorsNois_N", "Norm_ErrorsRepaired_ExDisf", "TotalErrorsNormal_exDisf", "Noise_ErrorsRepaired_ExDisf", "TotalErrorsNoise_exDisf"],
"@context": "http://schema.org/",
"@type": "Dataset",
"variableMeasured": [
{
"name": "Age",
"@type": "propertyValue"
},
{
"name": "Length_Parkinson",
"@type": "propertyValue"
},
{
"name": "Medication",
"@type": "propertyValue"
},
{
"name": "H_Y",
"@type": "propertyValue"
},
{
"name": "Sex",
"value": "0. male,\n1. female",
"maxValue": "1",
"minValue": "0",
"@type": "propertyValue"
},
{
"name": "Handedness",
"@type": "propertyValue"
},
{
"name": "MMSE",
"@type": "propertyValue"
},
{
"name": "RAVEN",
"@type": "propertyValue"
},
{
"name": "Phonologic_Fluency",
"@type": "propertyValue"
},
{
"name": "Semantic_Fluency",
"@type": "propertyValue"
},
{
"name": "BNT",
"@type": "propertyValue"
},
{
"name": "SyllSec_Norm",
"@type": "propertyValue"
},
{
"name": "SyllSec_Ruis",
"@type": "propertyValue"
},
{
"name": "NPro_Dur_Norm",
"@type": "propertyValue"
},
{
"name": "NPro_Dur_Ruis",
"@type": "propertyValue"
},
{
"name": "Nrm_Reformulation",
"@type": "propertyValue"
},
{
"name": "Nrm_FilledPause",
"@type": "propertyValue"
},
{
"name": "Nrm_Repetition",
"@type": "propertyValue"
},
{
"name": "DisfluenciesNorm",
"@type": "propertyValue"
},
{
"name": "Nois_Reformulation",
"@type": "propertyValue"
},
{
"name": "Nois_FilledPause",
"@type": "propertyValue"
},
{
"name": "Nois_Repetition",
"@type": "propertyValue"
},
{
"name": "DisfluenciesNoise",
"@type": "propertyValue"
},
{
"name": "Nrm_Sem_Unrep",
"@type": "propertyValue"
},
{
"name": "Nrm_Sem_Rep",
"@type": "propertyValue"
},
{
"name": "SemErrors_Norm",
"@type": "propertyValue"
},
{
"name": "Nrm_Phon_Unrep",
"@type": "propertyValue"
},
{
"name": "Nrm_Phon_Rep",
"@type": "propertyValue"
},
{
"name": "PhonErrors_Norm",
"@type": "propertyValue"
},
{
"name": "Nrm_Gram_Unrep",
"@type": "propertyValue"
},
{
"name": "Nrm_Gram_Rep",
"@type": "propertyValue"
},
{
"name": "GramErrors_Norm",
"@type": "propertyValue"
},
{
"name": "Norm_ErrorsUnrepaired",
"@type": "propertyValue"
},
{
"name": "Norm_ErrorsRepaired",
"@type": "propertyValue"
},
{
"name": "TotalErrorsNorm_N",
"@type": "propertyValue"
},
{
"name": "Nois_Sem_Unrep",
"@type": "propertyValue"
},
{
"name": "Nois_Sem_Rep",
"@type": "propertyValue"
},
{
"name": "SemErrors_Noise",
"@type": "propertyValue"
},
{
"name": "Nois_Phon_Unrep",
"@type": "propertyValue"
},
{
"name": "Nois_Phon_Rep",
"@type": "propertyValue"
},
{
"name": "PhonErrors_Nois",
"@type": "propertyValue"
},
{
"name": "Nois_Gram_Unrep",
"@type": "propertyValue"
},
{
"name": "Nois_Gram_Rep",
"@type": "propertyValue"
},
{
"name": "GramErrors_Nois",
"@type": "propertyValue"
},
{
"name": "Noise_ErrorsUnrepaired",
"@type": "propertyValue"
},
{
"name": "Noise_ErrorsRepaired",
"@type": "propertyValue"
},
{
"name": "TotalErrorsNois_N",
"@type": "propertyValue"
},
{
"name": "Norm_ErrorsRepaired_ExDisf",
"@type": "propertyValue"
},
{
"name": "TotalErrorsNormal_exDisf",
"@type": "propertyValue"
},
{
"name": "Noise_ErrorsRepaired_ExDisf",
"@type": "propertyValue"
},
{
"name": "TotalErrorsNoise_exDisf",
"@type": "propertyValue"
}
]
}`